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Title page for ETD etd-11172016-133216

Type of Document

Master's Thesis

Author

Geanes, Alexander Richard

URN

etd-11172016-133216

Title

Development and application of ligand-based computational methods for de-novo drug design and virtual screening

Degree

Master of Science

Department

Chemistry

Advisory Committee

Advisor Name

Title

Craig Lindsley

Committee Co-Chair

Jens Meiler

Committee Co-Chair

Keywords

molecular design

drug discovery

focused libraries

de-novo

muscarinic receptor

ligand-based

machine learning

Date of Defense

2016-11-16

Availability

unrestricted

Abstract

Ligand-based computational drug discovery (LB-CADD) methods have been used widely over the last several decades to aid medicinal chemistry campaigns via virtual high-throughput screening (vHTS) and de-novo molecular design. A new de-novo drug design algorithm, BCL::EvoGen, based on a stochastic search algorithm was implemented within the BioChemical Library developed at Vanderbilt University. The EvoGen algorithm leverages reaction-based structure modification methods to iteratively build chemical structures, and ligand-based molecule scoring functions to guide molecular design. Results indicate that the EvoGen algorithm is capable of designing high-scoring molecules with novel and chemically reasonable structures. In a second study, LB-CADD models were used to prioritize a subset of a compound library the discovery of muscarinic acetylcholine receptor M5 negative allosteric modulators. An orthosteric antagonist VU0549108 (VU108) was discovered which exhibited an M5 IC50 of 5.23 uM and moderate selectivity across other muscarinic receptors. In addition, VU108 contains a novel chemical scaffold not previously associated with muscarinic receptor ligands.